[R-meta] F test vs QM test for test of moderators
Yefeng Yang
ye|eng@y@ng1 @end|ng |rom un@w@edu@@u
Sat Apr 29 07:49:34 CEST 2023
Dear Huang
If you understand what QM test is, it is not difficult to find out "Test of Moderators (coefficients 2:3)" printed below your output is the QM test results. Your test statistic was tested against F distribution. You can also use chi-square distribution (but not recommended). In essence, QM test is a sort of omnibus test or joint null-hypothesis test.
Best,
Yefeng
________________________________
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> on behalf of Huang Wu via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org>
Sent: Saturday, 29 April 2023 12:34
To: R meta <r-sig-meta-analysis using r-project.org>
Cc: Huang Wu <wuhuang0421 using gmail.com>
Subject: [R-meta] F test vs QM test for test of moderators
Dear all,
I am writing to ask about the Test of Moderators in meta-analysis.
Specifically, I am curious about the appropriate test to use between the F
test and QM test. I ran the following code and obtained results using the F
test for the Test of Moderators. However, I would like to explore how to
obtain QM test results.
Could you kindly advise me on the suitable test to use and how to obtain QM
test results using the metafor package?
Thank you for your assistance.
Huang
----------------------------------------------------
USnew_c.Dnoctl.model <- rma.mv(yi=effect_d, #effect size
V = VUSnew_c.Dnoctl, #variance (tHIS IS WHAt
CHANGES FROM HEmodel)
mods = ~ grade_level,
random = ~1 | ID/eid, #nesting structure
test= "t", #use t-tests
data=USnew_c.Dnoctl, #define data
method="REML") #estimate variances using REML
summary(USnew_c.Dnoctl.model)
----------------------------------------------------
Multivariate Meta-Analysis Model (k = 142; method: REML)
logLik Deviance AIC BIC AICc
16.9680 -33.9361 -23.9361 -9.2637 -23.4849
Variance Components:
estim sqrt nlvls fixed factor
sigma^2.1 0.0300 0.1733 19 no ID
sigma^2.2 0.0161 0.1270 142 no ID/eid
Test for Residual Heterogeneity:
QE(df = 139) = 344.3018, p-val < .0001
Test of Moderators (coefficients 2:3):
F(df1 = 2, df2 = 139) = 1.1327, p-val = 0.3251
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